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01-12-2022 | Myocardial Infarction | Research article

Nonlinear causal effects of estimated glomerular filtration rate on myocardial infarction risks: Mendelian randomization study

Authors: Sehoon Park, Soojin Lee, Yaerim Kim, Semin Cho, Hyeok Huh, Kwangsoo Kim, Yong Chul Kim, Seung Seok Han, Hajeong Lee, Jung Pyo Lee, Kwon Wook Joo, Chun Soo Lim, Yon Su Kim, Dong Ki Kim

Published in: BMC Medicine | Issue 1/2022

Abstract

Background

Previous observational studies suggested that a reduction in estimated glomerular filtration rate (eGFR) or a supranormal eGFR value was associated with adverse cardiovascular risks. However, a previous Mendelian randomization (MR) study under the linearity assumption reported null causal effects from eGFR on myocardial infarction (MI) risks. Further investigation of the nonlinear causal effect of kidney function assessed by eGFR on the risk of MI by nonlinear MR analysis is warranted.

Methods

In this MR study, genetic instruments for log-eGFR based on serum creatinine were developed from European samples included in the CKDGen genome-wide association study (GWAS) meta-analysis (N=567,460). Alternate instruments for log-eGFR based on cystatin C were developed from a GWAS of European individuals that included the CKDGen and UK Biobank data (N=460,826). Nonlinear MR analysis for the risk of MI was performed using the fractional polynomial method and the piecewise linear method on data from individuals of white British ancestry in the UK Biobank (N=321,024, with 12,205 MI cases).

Results

Nonlinear MR analysis demonstrated a U-shaped (quadratic P value < 0.001) association between MI risk and genetically predicted eGFR (creatinine) values, as MI risk increased as eGFR declined in the low eGFR range and the risk increased as eGFR increased in the high eGFR range. The results were similar even after adjustment for clinical covariates, such as blood pressure, diabetes mellitus, dyslipidemia, or urine microalbumin levels, or when genetically predicted eGFR (cystatin C) was included as the exposure.

Conclusion

Genetically predicted eGFR is significantly associated with the risk of MI with a parabolic shape, suggesting that kidney function impairment, either by reduced or supranormal eGFR, may be causally linked to a higher MI risk.

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Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. https://doi.org/10.1016/s0140-6736(20)30045-3.CrossRef

Meisinger C, Döring A, Löwel H. Chronic kidney disease and risk of incident myocardial infarction and all-cause and cardiovascular disease mortality in middle-aged men and women from the general population. Eur Heart J. 2006;27(10):1245–50. https://doi.org/10.1093/eurheartj/ehi880.CrossRefPubMed

Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith JL, et al. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol. 2003;41(1):47–55. https://doi.org/10.1016/s0735-1097(02)02663-3.CrossRefPubMed

Inrig JK, Gillespie BS, Patel UD, Briley LP, She L, Easton JD, et al. Risk for cardiovascular outcomes among subjects with atherosclerotic cardiovascular disease and greater-than-normal estimated glomerular filtration rate. Clin J Am Soc Nephrol. 2007;2(6):1215–22. https://doi.org/10.2215/cjn.00930207.CrossRefPubMed

Kanbay M, Ertuglu LA, Afsar B, Ozdogan E, Kucuksumer ZS, Ortiz A, et al. Renal hyperfiltration defined by high estimated glomerular filtration rate: a risk factor for cardiovascular disease and mortality. Diabetes Obes Metab. 2019;21(11):2368–83. https://doi.org/10.1111/dom.13831.CrossRefPubMed

Tonelli M, Klarenbach SW, Lloyd AM, James MT, Bello AK, Manns BJ, et al. Higher estimated glomerular filtration rates may be associated with increased risk of adverse outcomes, especially with concomitant proteinuria. Kidney Int. 2011;80(12):1306–14. https://doi.org/10.1038/ki.2011.280.CrossRefPubMed

Matsushita K, Coresh J, Sang Y, Chalmers J, Fox C, Guallar E, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015;3(7):514–25. https://doi.org/10.1016/s2213-8587(15)00040-6.CrossRefPubMedPubMedCentral

van der Laan SW, Fall T, Soumaré A, Teumer A, Sedaghat S, Baumert J, et al. Cystatin C and cardiovascular disease: a Mendelian randomization study. J Am Coll Cardiol. 2016;68(9):934–45. https://doi.org/10.1016/j.jacc.2016.05.092.CrossRefPubMedPubMedCentral

Charoen P, Nitsch D, Engmann J, Shah T, White J, Zabaneh D, et al. Mendelian Randomisation study of the influence of eGFR on coronary heart disease. Sci Rep. 2016;6(1):28514. https://doi.org/10.1038/srep28514.CrossRefPubMedPubMedCentral

10.

Staley JR, Burgess S. Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization. Genet Epidemiol. 2017;41(4):341–52. https://doi.org/10.1002/gepi.22041.CrossRefPubMedPubMedCentral

11.

Reboldi G, Verdecchia P, Fiorucci G, Beilin LJ, Eguchi K, Imai Y, et al. Glomerular hyperfiltration is a predictor of adverse cardiovascular outcomes. Kidney Int. 2018;93(1):195–203. https://doi.org/10.1016/j.kint.2017.07.013.CrossRefPubMed

12.

Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019;51(6):957–72. https://doi.org/10.1038/s41588-019-0407-x.CrossRefPubMedPubMedCentral

13.

Stanzick KJ, Li Y, Schlosser P, Gorski M, Wuttke M, Thomas LF, et al. Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals. Nat Commun. 2021;12(1):4350. https://doi.org/10.1038/s41467-021-24491-0.CrossRefPubMedPubMedCentral

14.

Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z.CrossRefPubMedPubMedCentral

15.

Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40(7):597–608. https://doi.org/10.1002/gepi.21998.CrossRefPubMedPubMedCentral

16.

Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj. 2018;362:k601. https://doi.org/10.1136/bmj.k601.CrossRefPubMedPubMedCentral

17.

Park S, Lee S, Kim Y, Cho S, Kim K, Kim YC, et al. Kidney function and obstructive lung disease: a bidirectional Mendelian randomisation study. Eur Respir J. 2021;58(6):2100848. https://doi.org/10.1183/13993003.00848-2021.CrossRefPubMed

18.

Park S, Lee S, Kim Y, Cho S, Kim K, Kim YC, et al. A Mendelian randomization study found causal linkage between telomere attrition and chronic kidney disease. Kidney Int. 2021;100(5):1063–70. https://doi.org/10.1016/j.kint.2021.06.041.CrossRefPubMed

19.

Park S, Lee S, Kim Y, Lee Y, Kang MW, Kim K, et al. Atrial fibrillation and kidney function: a bidirectional Mendelian randomization study. Eur Heart J. 2021;42(29):2816–23. https://doi.org/10.1093/eurheartj/ehab291.CrossRefPubMed

20.

Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(1):7. https://doi.org/10.1186/s13742-015-0047-8.CrossRefPubMedPubMedCentral

21.

Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40(2):221–6. https://doi.org/10.1053/ajkd.2002.34487.CrossRefPubMed

22.

Lees JS, Welsh CE, Celis-Morales CA, Mackay D, Lewsey J, Gray SR, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25(11):1753–60. https://doi.org/10.1038/s41591-019-0627-8.CrossRefPubMedPubMedCentral

23.

Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64. https://doi.org/10.1093/ije/dyr036.CrossRefPubMed

24.

Malik R, Georgakis MK, Vujkovic M, Damrauer SM, Elliott P, Karhunen V, et al. Relationship between blood pressure and incident cardiovascular disease: linear and nonlinear Mendelian randomization analyses. Hypertension. 2021;77(6):2004–13. https://doi.org/10.1161/hypertensionaha.120.16534.CrossRefPubMed

25.

Sun YQ, Burgess S, Staley JR, Wood AM, Bell S, Kaptoge SK, et al. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses. Bmj. 2019;364:l1042. https://doi.org/10.1136/bmj.l1042.CrossRefPubMedPubMedCentral

26.

Choi SW, Mak TS, O'Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15(9):2759–72. https://doi.org/10.1038/s41596-020-0353-1.CrossRefPubMedPubMedCentral

27.

Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–9. https://doi.org/10.1056/NEJMoa1114248.CrossRefPubMedPubMedCentral

28.

Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. https://doi.org/10.7326/0003-4819-150-9-200905050-00006.CrossRefPubMedPubMedCentral

29.

Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. https://doi.org/10.1002/gepi.21965.CrossRefPubMedPubMedCentral

30.

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. https://doi.org/10.1093/ije/dyv080.CrossRefPubMedPubMedCentral

31.

Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186. https://doi.org/10.12688/wellcomeopenres.15555.2.CrossRefPubMed

32.

Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33. https://doi.org/10.1038/ng.2480.CrossRefPubMed

33.

Park M, Yoon E, Lim YH, Kim H, Choi J, Yoon HJ. Renal hyperfiltration as a novel marker of all-cause mortality. J Am Soc Nephrol. 2015;26(6):1426–33. https://doi.org/10.1681/asn.2014010115.CrossRefPubMed

34.

Kim Y, Lee S, Lee Y, Park S, Park S, Paek JH, et al. The minimum-mortality estimated glomerular filtration rate percentile shifts upward in the aged population: a nationwide population-based study. Clin Kidney J. 2021;14(5):1356–63. https://doi.org/10.1093/ckj/sfaa238.CrossRefPubMed

35.

Eknoyan G, Lameire N, Eckardt K, Kasiske B, Wheeler D, Levin A, et al. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2013;3(1):5–14. https://doi.org/10.1038/kisup.2012.65.CrossRef

36.

Batra J, Buttar RS, Kaur P, Kreimerman J, Melamed ML. FGF-23 and cardiovascular disease: review of literature. Curr Opin Endocrinol Diabetes Obes. 2016;23(6):423–9. https://doi.org/10.1097/med.0000000000000294.CrossRefPubMedPubMedCentral

37.

Fliser D, Kollerits B, Neyer U, Ankerst DP, Lhotta K, Lingenhel A, et al. Fibroblast growth factor 23 (FGF23) predicts progression of chronic kidney disease: the Mild to Moderate Kidney Disease (MMKD) Study. J Am Soc Nephrol. 2007;18(9):2600–8. https://doi.org/10.1681/asn.2006080936.CrossRefPubMed

38.

Angiolillo DJ, Bernardo E, Capodanno D, Vivas D, Sabaté M, Ferreiro JL, et al. Impact of chronic kidney disease on platelet function profiles in diabetes mellitus patients with coronary artery disease taking dual antiplatelet therapy. J Am Coll Cardiol. 2010;55(11):1139–46. https://doi.org/10.1016/j.jacc.2009.10.043.CrossRefPubMed

39.

Baaten C, Sternkopf M, Henning T, Marx N, Jankowski J, Noels H. Platelet Function in CKD: A Systematic Review and Meta-Analysis. J Am Soc Nephrol. 2021;32(7):1583–98. https://doi.org/10.1681/asn.2020101440.CrossRef

40.

Gremmel T, Müller M, Steiner S, Seidinger D, Koppensteiner R, Kopp CW, et al. Chronic kidney disease is associated with increased platelet activation and poor response to antiplatelet therapy. Nephrol Dial Transplant. 2013;28(8):2116–22. https://doi.org/10.1093/ndt/gft103.CrossRefPubMed

41.

Paloian NJ, Giachelli CM. A current understanding of vascular calcification in CKD. Am J Physiol Renal Physiol. 2014;307(8):F891–900. https://doi.org/10.1152/ajprenal.00163.2014.CrossRefPubMedPubMedCentral

42.

Eriksen BO, Løchen ML, Arntzen KA, Bertelsen G, Eilertsen BA, von Hanno T, et al. Subclinical cardiovascular disease is associated with a high glomerular filtration rate in the nondiabetic general population. Kidney Int. 2014;86(1):146–53. https://doi.org/10.1038/ki.2013.470.CrossRefPubMed

43.

Mathisen UD, Melsom T, Ingebretsen OC, Jenssen T, Njølstad I, Solbu MD, et al. Estimated GFR associates with cardiovascular risk factors independently of measured GFR. J Am Soc Nephrol. 2011;22(5):927–37. https://doi.org/10.1681/asn.2010050479.CrossRefPubMedPubMedCentral

44.

Melsom T, Fuskevåg OM, Mathisen UD, Strand H, Schei J, Jenssen T, et al. Estimated GFR is biased by non-traditional cardiovascular risk factors. Am J Nephrol. 2015;41(1):7–15. https://doi.org/10.1159/000371557.CrossRefPubMed

45.

Bjornstad P, Cherney DZ, Snell-Bergeon JK, Pyle L, Rewers M, Johnson RJ, et al. Rapid GFR decline is associated with renal hyperfiltration and impaired GFR in adults with Type 1 diabetes. Nephrol Dial Transplant. 2015;30(10):1706–11. https://doi.org/10.1093/ndt/gfv121.CrossRefPubMedPubMedCentral

46.

Vallon V. The proximal tubule in the pathophysiology of the diabetic kidney. Am J Physiol Regul Integr Comp Physiol. 2011;300(5):R1009–22. https://doi.org/10.1152/ajpregu.00809.2010.CrossRefPubMed

47.

Zelniker TA, Wiviott SD, Raz I, Im K, Goodrich EL, Bonaca MP, et al. SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet. 2019;393(10166):31–9. https://doi.org/10.1016/s0140-6736(18)32590-x.CrossRefPubMed

48.

Savarese G, Costanzo P, Cleland JGF, Vassallo E, Ruggiero D, Rosano G, et al. A meta-analysis reporting effects of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers in patients without heart failure. J Am Coll Cardiol. 2013;61(2):131–42. https://doi.org/10.1016/j.jacc.2012.10.011.CrossRefPubMed

49.

Sochett EB, Cherney DZ, Curtis JR, Dekker MG, Scholey JW, Miller JA. Impact of renin angiotensin system modulation on the hyperfiltration state in type 1 diabetes. J Am Soc Nephrol. 2006;17(6):1703–9. https://doi.org/10.1681/asn.2005080872.CrossRefPubMed

50.

van Bommel EJM, Muskiet MHA, van Baar MJB, Tonneijck L, Smits MM, Emanuel AL, et al. The renal hemodynamic effects of the SGLT2 inhibitor dapagliflozin are caused by post-glomerular vasodilatation rather than pre-glomerular vasoconstriction in metformin-treated patients with type 2 diabetes in the randomized, double-blind RED trial. Kidney Int. 2020;97(1):202–12. https://doi.org/10.1016/j.kint.2019.09.013.CrossRefPubMed

51.

Burgess S, Butterworth A, Malarstig A, Thompson SG. Use of Mendelian randomisation to assess potential benefit of clinical intervention. BMJ. 2012;345:e7325. https://doi.org/10.1136/bmj.e7325.CrossRefPubMed

52.

Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34. https://doi.org/10.1093/aje/kwx246.CrossRefPubMedPubMedCentral

Title: Nonlinear causal effects of estimated glomerular filtration rate on myocardial infarction risks: Mendelian randomization study
Authors: Sehoon Park
Soojin Lee
Yaerim Kim
Semin Cho
Hyeok Huh
Kwangsoo Kim
Yong Chul Kim
Seung Seok Han
Hajeong Lee
Jung Pyo Lee
Kwon Wook Joo
Chun Soo Lim
Yon Su Kim
Dong Ki Kim
Publication date: 01-12-2022
Publisher: BioMed Central
Keyword: Myocardial Infarction
Published in: BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-022-02251-1

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Nonlinear causal effects of estimated glomerular filtration rate on myocardial infarction risks: Mendelian randomization study

Abstract

Background

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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